Objective:

In this project we investigate the acute and chronic effects of air pollution on cognitive and neurological impairments, systemic inflammation, and vascular dysfunction. We examine how these effects differ depending on the composition of multi-pollutant mixtures and the source contributions to PM composition. Also we ascertain the level of increased effects in susceptible and vulnerable subpopulations by examining modifying factors of obesity, diabetes, diet, socioeconomic position, and psychosocial stress.

Progress Summary:

We have made good progress with our NAS cohort as well as satellite based exposure models which we present below:

Results using Central Site Monitoring: In Madrigano et al (2012) we reported that exposure to Black Carbon (BC) particles and PM2.5 in the Normative Aging Study were associated with decreased methylation of iNOS, but not of the glucocorticoid receptor, and that subjects with low optimism and high anxiety had effects that were 3-4 times larger.

In Bind (2012) we found effects of particle number, BC, nitrogen dioxide (NO2), and carbon monoxide (CO) on fibrinogen concentrations. Ozone (O3) was a predictor of C-reactive protein (CRP) and ICAM-1 in the NAS. Particle number, BC, NO2, CO, PM2.5, and sulfates were associated with ICAM-1 and VCAM-1. When we examined effect modification by DNA methylation status and we found stronger effects in subjects with lower LINE-1 or higher Alu methylation. Lower methylation at one position in the promoter of factor III (F3) was associated with a stronger effect of air pollution on fibrinogen levels, while lower methylation of TLR-2 was associated with stronger effects on CRP concentrations. This establishes that gene specific methylation state can be a modifier of the health effects of air pollution.

In Sofer (2012) we introduced a method of gene selection for genome-wide methylation studies. The method is based on the continuous nature of methylation data, as opposed to genetic polymorphism data, and uses supervised canonical correlation analyses. In Schwartz (2013) we used genome-wide methylation from the Nimblegen array to demonstrate that exposure to BC and sulfate particles was associated with changes in DNA methylation along the asthma pathway, based on KEGG. This provides an important clue to why epidemiologic studies have reported associations of traffic pollution with asthma.

In Valdez, we expanded a method we previously used to examine interactions between monthly species specific mass ratios to PM2.5 mass and PM2.5 as modifiers of the particle effects on mortality in a daily time series.

In Von Klot et al we showed that control for influenza epidemics explained the irregular part of seasonal variability in cardiovascular mortality, allowing seasonal control to be established with half the degrees of freedom, but a better fit, than with traditional models with e.g., 6 df per year of seasonal control using splines. This offers a possibility of better separation of environmental exposures from seasonal patterns.

Results using spatiotemporal exposure models: In Nordio (2013) we showed out spatiotemporal AOD-LUR models work well for predicting PM10 as well as PM2.5, and work in very different topography than New England. This model is now being used to analyze health data in Italy.

In Kloog (2013) we combined our Spatiotemporal AID-LUR PM2.5 model with individual mortality data in Massachusetts geocoded to home address, and showed associations with both long- and short-term exposure. Unlike most cohort studies, which have samples highly unrepresentative of the general population, these results are for the entire population of Massachusetts. For short-term exposure, we found that for every 10-μg/m3 increase in PM2.5 exposure there was a 2.8% increase in PM-related mortality (95% confidence interval (CI) = 2.0–3.5). For the long-term exposure at the grid cell level, we found a Rate Ratio (RR) for every 10-μg/m3 increase of 1.6 (CI = 1.5–1.8) for particle-related diseases. Local PM2.5 had an RR of 1.4 (CI = 1.3–1.5), which was independent of and additive to the grid cell effect.

In Power (2013) we used our spatiotemporal BC exposure model to show that the association of BC with cognitive function in the Normative Aging Study was modified by polymorphisms in the hemochromatosis gene, which regulates metal uptake into cells. This suggests the involvement of metals associated with traffic particles in the cognitive association.

In Wilker (2013) we showed that long-term BC exposure was associated with coronary artery Intima Media Thickness, indicating that traffic particles play a key role in atherosclerosis.

In Kloog (2012) we demonstrated how AOD can be used reliably to predict daily PM2.5 mass concentrations in the Mid-Atlantic area, validating our previous model in New England. We have also shown how our model improves further by adding methodological improvements, allowing us to address some of the shortcoming of the ﬁrst iteration of the model. These included inverse probability weighting to address nonrandom missingness, sub regions, and a shrinkage approach to covariates. For all days without AOD values, model performance was excellent (mean “out-of-sample” R2=0.81, year-to-year variation 0.79−0.84). Upon removal of outliers in the PM2.5 monitoring data, the results of the cross- validation procedure was even better (overall mean ”out of sample” R2 of 0.85). Further, cross validation results revealed no bias in the predicted concentrations (Slope of observed versus predicted = 0.97−1.01). We are now applying this exposure to administrative data.

Future Activities:

We will continue our work on exploring multi-pollutant mixtures by examining the effect of metals, EC, and OC in a national study of cities with speciation data. This will also include meta-analyses to examine effect modification by temperature and other city characteristics. We will apply our cluster analyses, and our daily XRF data, and look at health effects of clusters and metals in the NAS, and we will expand our examination of pollution effects on DNA methylation, using the Illumina 450k array. We will further refine our PM2.5 model using 1km MAIAC data and apply it to the NAS and other cohorts.

Main Center Abstract and Reports:

Subprojects under this Center:(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).R834798C001 Relative Toxicity of Air Pollution MixturesR834798C002 Cognitive Decline, Cardiovascular Changes, and Biological Aging in Response to Air PollutionR834798C003 Identifying the Cognitive and Vascular Effects of Air Pollution Sources and Mixtures in the Framingharn Offspring and Third Generation CohortsR834798C004 Longitudinal Effects of Multiple Pollutants on Child Growth, Blood Pressure and CognitionR834798C005 A National Study to Assess Susceptibility, Vulnerability, and Effect Modification of Air Pollution Health Risks

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.